Multi-view score fusion for content-based mammogram retrieval

Sami Dhahbi; Walid Barhoumi; Ezzeddine Zagrouba

doi:10.1117/12.2228614

8 December 2015 Multi-view score fusion for content-based mammogram retrieval

Sami Dhahbi, Walid Barhoumi, Ezzeddine Zagrouba

Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 987515 (2015) https://doi.org/10.1117/12.2228614
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

Abstract

Screening mammography provides two views for each breast: Medio-Lateral Oblique (MLO) and Cranial-Caudal (CC) views. However, current content based image retrieval (CBIR) systems analyze each view independently, in spite of their complementarities. To further improve the retrieval performance, this paper introduces a two-view CBIR system that combines retrieval results of MLO and CC views. First, we computed the similarity scores between MLO (resp. CC) ROIs in the database and the MLO (resp. CC) query ROI. These ROIs are characterized using curvelet moments. Then, a new linear weighted sum scheme combines MLO and CC scores; it assigns weights for each view according to the distribution of the classes of its neighbors. The ROIs having the highest fused scores are displayed to the radiologist and used to compute the malignancy likelihood of the lesion. Experiments performed on mammograms from the Digital Database for Screening Mammography (DDSM) show the effectiveness of the proposed method.

Citation Download Citation

Sami Dhahbi, Walid Barhoumi, and Ezzeddine Zagrouba "Multi-view score fusion for content-based mammogram retrieval", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987515 (8 December 2015); https://doi.org/10.1117/12.2228614

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